Download PDFOpen PDF in browserEvolutionary Computation to Estimate VolatilityEasyChair Preprint no. 3017 pages•Date: June 24, 2018AbstractA performance evaluation study is implemented among the methods of Genetic Algorithms with Floating Point representation and some traditional optimization methods in the task of estimating the parameters of a GARCH (1,1) Normal process using artificial data obtained by simulation. The results show that the approximate solutions obtained by means of Genetic Algorithms present a better stability and precision with respect to the traditional optimization methods. The choice of the initial point in the numerical optimization methods is not a critical condition in the use of the Genetic Algorithms as a method to find the solution. Finally, the use of the method of Genetic Algorithms in the finding of the solution of the vector of parameters of the likelihood function of a model GARCH (1,1) t-Student for data of rates of exchange returns of the Sol versus the Dollar. Keyphrases: Algoritmos Genéticos, GARCH, Inferencia estadística
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